Regarding the content analysis to extract users' affective state, beliefs and opinions, similar approaches are viable.
Affective Computing aims to recognize, infer and interpret human emotions [1]}, distinguishing between sentiment analysis, the polarity of content (e.g. [2]}, [3]}, [4]}), and emotion recognition, the emotions present in a piece of information (e.g. [5]}, [6]}). In comparison, Opinion Extraction aims at discovering user's interests and their corresponding opinions [7]}.
In general, the systems extract the entity or the target, the aspect of the entity, the opinion holder, the time when the opinion was expressed, and the opinion [8]}.
Similarly, the positive aspects of social media interaction, crucial for estimating the CWB, could be extracted. Still, they have attracted less attention, but see [9]}, [10]}.
